Metabolic gene signature for predicting breast cancer recurrence using transcriptome analysis

Area of Science:

• Oncology
• Genomics
• Metabolomics

Background:

• Breast cancer recurrence risk stratification is crucial for personalized treatment.
• Identifying reliable biomarkers for predicting recurrence is an ongoing challenge in breast cancer research.

Purpose of the Study:

• To identify a metabolic gene signature for stratifying breast cancer recurrence risk.
• To develop a predictive model for recurrence-free survival in breast cancer patients.

Main Methods:

• Utilized data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases.
• Employed the limma package to identify differentially expressed metabolic genes.
• Constructed a five-gene metabolic signature and a nomogram for survival prediction.

Main Results:

• A five-gene metabolic signature demonstrated high accuracy and predictive power in training and validation cohorts.
• The established nomogram, integrating the risk score and clinicopathological features, effectively predicted recurrence-free survival.
• The signature showed significant prognostic value for breast cancer recurrence.

Conclusions:

• The developed metabolic gene signature offers a promising tool for breast cancer recurrence risk stratification.
• The nomogram provides a valuable clinical aid for predicting patient outcomes and guiding treatment decisions.
• This approach may enhance the precision of recurrence risk assessment in breast cancer management.